Hasmat Malik PPT-12110307

APresentation

on

Artificial Intelligence (AI) and its Applications in Gear Fault Detection

By

Hasmat Malik (Enrollment No. 12110307)

Mechanical Engineering Department, IIT Indore

Contents1. Introduction

2. Vibration Condition Monitoring

3. Artificial Intelligence (AI) Technique

4. Literature Review

5. Proposed Work Plan

6. Conclusion

7. Future Work

References01/12/2012 2

Gear is one of the most important mechanical elements in power

transmission.

It is used to transmit motion and/or torque mechanically b/w

parallel, intersecting or non-intersecting shafts

Power transmission using gears result in cyclic loading for the

individual gear tooth. Cyclic loading may leads to various gear

faults

1. Introduction

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Cracked tooth Brocken tooth Chipped tooth Missing tooth Spalling tooth and Worn tooth

Demand for Gear fault diagnosis &condition monitoring is increasing day byday.

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Example of gear tooth faults Cont..

Vibration signals is most commonly used method in condition

monitoring.

Diagnosing a gear system by examining the vibration signals is

the most commonly used method for detecting gear failures.

Vibration signal encountered in machines and such as machine

tool and gear box can be classified as:

Stationary

Non-stationary

2. Vibration Condition Monitoring

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The conventional methods for processing measured

vibration data are:

Time-domain technique

Frequency-domain technique

Time-frequency-domain technique

Cont..

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AI is the science and engineering of making intelligent machines,

specially intelligent computer programs.

3. Artificial Intelligence (AI) Technique

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Different AI Techniques are as follows:

Fuzzy logic (FL)

Artificial neural network (ANN)

Support vector machine (SVM)

Genetic algorithm (GA)

Genetic programming (GP)

Swarm optimization

Cont..

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AI Techniques Area Number of Paper

Neural Networks (NN) Comparison of neural classifiers for vehicles gear estimation

Gear fault classification

2

Fuzzy-Logic (FL) Gear fault diagnosis and condition assessment 4

Support vector Machine

(SVM)

Gear fault/Oil Analysis and condition monitoring

Processing of end effects of HHT

5

ANN with EMD Condition monitoring 1

Neuro-Fuzzy (ANFIS) Gear system monitoring and Fault identification and classification

A hybrid tool for detection of bearing faults

3

ANN with SVM Model for condition monitoring of transformer

Incipient gear box fault diagnosis

Fault diagnosis of spur bevel gear box

3

SVM with PSO Gear fault classification 1

Expert system-ES Fault Diagnosis for Gear Box 1

ANN with ES Automated on-line monitoring and fault diagnosis system 7

4. Literature Review

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Wavelet Transform-WT Efficient fault diagnosis system 20

Wavelet with ANN Faulted gear identification 2

Wavelet with SVM Fault diagnosis of spur bevel gear box

Fault analysis of bearings

3

GA Bearing fault diagnosis 1

GA with SVM A novel fault diagnosis model for gear box 1

NSGA Fault diagnosis of gearbox 1

ANN-SVM with GA Gear fault detection 1

ANN with GA Gear/Gearbox fault detection

Hybrid system for gear fault diagnosis

4

EMD Gear Fault Diagnosis

Gearbox condition monitoring

12

Improved EMD Fault signature analysis

IMF selection criterion

6

EDM with SVM A Novel Intelligent Gear Fault Diagnosis 2

Cont..

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Research Gap in AI Techniques

1. AI Techniques for Fault Classification are:

Probabilistic Neural Network (PNN)

Learning Vector Quantization (LVQ)

Self-Organizing Map (SOM)

Gene Expression Programming (GEP)

Hybrid system

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Cont..

2. AI Techniques for Optimization are:

Ant Colony Optimization (ACO)

Particle Swarm Optimization (PSO)

Bee Colony Optimization (BCO)

Genetic Programming (GP)

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Cont..

A. Design Methodology for Fuzzy FaultDiagnosis System

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B. The Design Methodology of ANN FaultDiagnostic System

Neural Network System

Planning

NW Assigning

Assign NW Performance

Collect the Data

Create the Network

Configure the Network

Initialize the Weights and Biases

Train the Network

Validate the Network

Use the Network

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5. Proposed Work Plan

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Conclusions

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Literature survey of AI application in gear fault diagnosis

is presented.

Time-frequency feature extraction methods have been

explained which are used as a input feature in AI model.

AI based model is presented for gear fault detection.

Design methodology for Fuzzy-logic and ANN based gear

fault detection is presented.

Future Work

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Implementation of PNN, LVQ, SOM, SVM and hybrid model

for gear fault diagnosis

Implementation of parameter optimization system for ANN,

fuzzy logic and SVM.

To find the Co-relation between all purposed method for Gear

condition assessment and other new AI based techniques

1. Y. Lei, et al., A review on empirical mode decomposition in fault diagnosis of rotating machinery, Mech. Syst. Signal Process.

(2012), http://dx.doi.org/10.1016/j.ymssp.2012.09.015

2. Bi, Y., Guan, J., & Bell, D. (2008). The combination of multiple classifiers using an evidential reasoning approach. Artificial

Intelligence, 172, 1731–1751.

3. Halima, E. B., Shoukat Choudhury, M. A. A., Shah, S. L., & Zuo, M. J. (2008). Time domain averaging across all scales: A novel

method for detection of gearbox faults. Mechanical Systems and Signal Processing, 22, 261–278.

4. Hu, Q., Yu, D., & Xie, Z. (2008). Neighborhood classifiers. Expert Systems with Applications, 34, 866–876.

5. Lei, Y. G., He, Z. J., Zi, Y. Y., & Hu, Q. (2007). Fault diagnosis of rotating machinery based on multiple ANFIS combination with

GAs. Mechanical Systems and Signal Processing, 21, 2280–2294.

6. Lei, Y. G., He, Z. J., & Zi, Y. Y. (2008). A new approach to intelligent fault diagnosis of rotating machinery. Expert Systems with

Applications, 35, 1593–1600.

7. Pal, S. K., Bandyopadhyay, S., & Murthy, C. A. (1998). Genetic algorithms for generation of class boundaries. IEEE Transactions

on Systems, Man, Cybernetics, 28, 816–828.

8. Peng, Z. K., & Chu, F. L. (2003). Application of wavelet transform in machine condition monitoring and fault diagnostics: A review

with bibliography. Mechanical Systems and Signal Processing, 17, 199–221.

9. Rafiee, J., Arvani, F., Harifi, A., & Sadeghi, M. H. (2007). Intelligent condition monitoring of a gearbox using artificial neural

network. Mechanical Systems and Signal Processing, 21, 1746–1754.

10. Samanta, B. (2004). Gear fault detection using artificial neural networks and support vector machines with genetic algorithms.

Mechanical Systems and Signal Processing, 18, 625–644.

References

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THANK YOU

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